Accelerated urbanization increases both the frequency and intensity of heatwaves (HW) and urban heat islands (UHIs). An extreme HW event occurred in 2012 summer that caused temperatures of more than 40°C in Chicago, Illinois, USA, which is a highly urbanized city impacted by UHIs. In this study, multiple numerical models, including the High Resolution Land Data Assimilation System (HRLDAS) and Weather Research and Forecasting (WRF) model, were used to simulate the HW and UHI, and their performance was evaluated. In addition, sensitivity testing of three different WRF configurations was done to determine the impact of increasing model complexity in simulating urban meteorology. Model performances were evaluated based on the statistical performance metrics, the application of a multi‐layer urban canopy model (MLUCM) helps WRF to provide the best performance in this study. HW caused rural temperatures to increase by ∼4°C, whereas urban Chicago had lower magnitude increases from the HW (∼2–3°C increases). Nighttime UHI intensity (UHII) ranged from 1.44 to 2.83°C during the study period. Spatiotemporal temperature fields were used to estimate the potential heat‐related exposure and to quantify the Excessive Heat Factor (EHF). The EHF during the HW episode provides a risk map indicating that while urban Chicago had higher heat‐related stress during this event, the rural area also had high risk, especially during nighttime in central Illinois. This study provides a reliable method to estimate spatiotemporal exposures for future studies of heat‐related health impacts.
This content will become publicly available on June 1, 2024
Urban heat islands (UHIs) may increase the likelihood that frost sensitive plants will escape freezing nighttime temperatures in late spring and early fall. Using data from 151 temperature sensors in the Madison, Wisconsin, region during March 2012–October 2016, we found that during time periods when the National Weather Service (NWS) issued freeze warnings (threshold of 0.0°C) or frost advisories (threshold of 2.22°C) were valid, temperatures in Madison’s most densely populated, built-up areas often did not fall below the respective temperature thresholds. Urban locations had a mean minimum temperature of 0.72° and 1.39°C for spring and fall freeze warnings, respectively, compared to −0.52° and −0.53°C for rural locations. On average, 31% of the region’s land area experienced minimum temperatures above the respective temperature thresholds during freeze warnings and frost advisories, and the likelihood of temperatures falling below critical temperature thresholds increased as the distance away from core urban centers increased. The urban–rural temperature differences were greatest in fall compared to spring, and when sensor temperatures did drop below thresholds, the maximum time spent at or below thresholds was highest for rural locations during fall freeze warnings (6.2 h) compared to urban locations (4.8 h). These findings potentially have widely varying implications for the general public and industry. UHIs create localized, positive perturbations to nighttime temperatures that are difficult to account for in forecasts; therefore, freeze warnings and frost advisories may have varying degrees of verification in medium-sized cities like Madison, Wisconsin, that are surrounded by cropland and natural vegetation.
The purpose of this study was to understand whether the urban heat island effect in Madison, Wisconsin, creates localized temperature patterns where county-scale frost advisories and freeze warnings may not verify. Approximately one-third of Madison’s urban core area and most densely populated region experienced temperatures that were consistently above critical low temperature thresholds. This is important because gardening and crop management decisions are responsive to the perceived risk of cold temperatures in spring and fall that can damage or kill plants. These results suggest that urban warming presents forecast challenges to the issuance of frost advisories and freeze warnings, supporting the need for improved numerical weather prediction at higher spatial resolution to account for complex urban meteorology.
- Award ID(s):
- 2025982
- NSF-PAR ID:
- 10492707
- Publisher / Repository:
- Weather and Forecasting
- Date Published:
- Journal Name:
- Weather and Forecasting
- Volume:
- 38
- Issue:
- 6
- ISSN:
- 0882-8156
- Page Range / eLocation ID:
- 1011 to 1023
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
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Using these records, we calculated the date of peak spawning activity in a given lake-year as the day of maximum tribal harvest. Although we were unable to account for varying effort in these data, a preliminary analysis comparing spawning dates estimated using tribal harvest to those determined from standardized agency surveys in the same lake and year showed that they were highly correlated (Pearson’s correlation: r = 0.91, P < 0.001). For lakes that had walleye spawning data from both agency surveys and tribal harvest, we used the data source with the greatest number of observation years. 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